Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	31,912
2	Florida	29,028
3	Mississippi	27,871
4	Arizona	27,733
5	Alabama	25,709
6	Georgia	23,945
7	South Carolina	23,111
8	New York	22,591
9	Nevada	22,504
10	Tennessee	22,262
11	Texas	22,083
12	New Jersey	21,812
13	Rhode Island	20,719
14	Iowa	20,681
15	Arkansas	20,287
16	District of Columbia	19,825
17	Illinois	18,756
18	Massachusetts	18,648
19	Idaho	18,049
20	California	18,033
21	Maryland	17,986
22	Delaware	17,898
23	Nebraska	17,724
24	Utah	16,271
25	North Carolina	16,017
26	North Dakota	15,510
27	South Dakota	15,270
28	Kansas	14,899
29	Oklahoma	14,842
30	Connecticut	14,831
31	Indiana	14,300
32	Virginia	14,128
33	Missouri	14,049
34	Wisconsin	13,865
35	Minnesota	13,460
36	New Mexico	12,090
37	Kentucky	11,389
38	Michigan	11,331
39	Pennsylvania	10,845
40	Ohio	10,536
41	Puerto Rico	10,395
42	Washington	10,206
43	Colorado	10,036
44	Alaska	8,304
45	Montana	6,961
46	Wyoming	6,638
47	Oregon	6,335
48	Hawaii	5,965
49	West Virginia	5,719
50	New Hampshire	5,350
51	Maine	3,367
52	Vermont	2,602

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Dakota	373
2	North Dakota	308
3	Alabama	263
4	Iowa	256
5	Missouri	210
6	Oklahoma	208
7	Tennessee	205
8	South Carolina	196
9	Mississippi	185
10	Kansas	182
11	Arkansas	181
12	Minnesota	155
13	Georgia	154
14	North Carolina	153
15	Hawaii	150
16	Illinois	148
17	Nebraska	146
18	Kentucky	145
19	Nevada	144
20	Indiana	143
21	Louisiana	139
22	Texas	135
23	Puerto Rico	126
24	California	119
25	Delaware	118
26	Florida	118
27	Virginia	117
28	Idaho	114
29	Utah	113
30	Montana	110
31	Wisconsin	100
32	Alaska	91
33	Maryland	87
34	Ohio	86
35	Rhode Island	83
36	West Virginia	79
37	District of Columbia	66
38	Michigan	62
39	Washington	57
40	Arizona	53
41	Pennsylvania	52
42	Oregon	51
43	New Mexico	49
44	Massachusetts	45
45	Wyoming	45
46	New Jersey	37
47	Colorado	36
48	Connecticut	35
49	New York	34
50	Maine	22
51	Vermont	18
52	New Hampshire	14

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,795
2	New York	1,672
3	Massachusetts	1,314
4	Connecticut	1,252
5	Louisiana	1,064
6	Rhode Island	989
7	District of Columbia	860
8	Mississippi	830
9	Arizona	691
10	Michigan	676
11	Illinois	651
12	Delaware	621
13	Maryland	621
14	Pennsylvania	604
15	South Carolina	528
16	Florida	520
17	Georgia	518
18	Indiana	489
19	Alabama	445
20	Texas	443
21	Nevada	423
22	New Mexico	371
23	Iowa	354
24	Ohio	354
25	Colorado	338
26	Minnesota	330
27	California	329
28	New Hampshire	317
29	Virginia	302
30	Arkansas	264
31	Missouri	264
32	Washington	263
33	North Carolina	260
34	Tennessee	252
35	Kentucky	216
36	Nebraska	207
37	Idaho	203
38	Oklahoma	202
39	Wisconsin	194
40	North Dakota	192
41	South Dakota	188
42	Kansas	154
43	Puerto Rico	135
44	Utah	127
45	West Virginia	119
46	Oregon	110
47	Maine	98
48	Montana	97
49	Vermont	92
50	Wyoming	63
51	Hawaii	48
52	Alaska	46

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	6
2	Alabama	5
3	Arkansas	4
4	Georgia	4
5	South Carolina	4
6	Florida	3
7	Louisiana	3
8	Arizona	2
9	Hawaii	2
10	Tennessee	2
11	Texas	2
12	West Virginia	2
13	California	1
14	Idaho	1
15	Iowa	1
16	Kentucky	1
17	Maryland	1
18	Massachusetts	1
19	Michigan	1
20	Missouri	1
21	Montana	1
22	Nevada	1
23	New Mexico	1
24	North Carolina	1
25	North Dakota	1
26	Oklahoma	1
27	Puerto Rico	1
28	Virginia	1
29	Alaska	0
30	Colorado	0
31	Connecticut	0
32	Delaware	0
33	District of Columbia	0
34	Illinois	0
35	Indiana	0
36	Kansas	0
37	Maine	0
38	Minnesota	0
39	Nebraska	0
40	New Hampshire	0
41	New Jersey	0
42	New York	0
43	Ohio	0
44	Oregon	0
45	Pennsylvania	0
46	Rhode Island	0
47	South Dakota	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	143,389	1	99
Lafayette	Florida	142,959	2	99
Lincoln	Arkansas	123,157	3	99
Lake	Tennessee	119,299	4	99
Lee	Arkansas	113,244	5	99
Davidson	Tennessee	37,420	159	94
Richland	South Carolina	26,924	362	88
Orange	California	15,284	1006	67
York	South Carolina	15,104	1030	67
Pierce	Washington	8,287	1856	40

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,612	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
Neshoba	Mississippi	3,434	5	99
Richland	South Carolina	471	679	78
Davidson	Tennessee	373	875	72
Orange	California	309	1037	66
Pierce	Washington	200	1404	55
York	South Carolina	167	1546	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.